cs.ROOct 27, 2026

Integrated Design and Reproducible Feasibility Analysis of a Four-Wheel Rover with Packet-Commanded Imaging, LiDAR Ranging, and Learning-Assisted Target Recognition

Authors: Sabik Bin Sultan, Shafi Bin Sultan, Safwan Sadad, Aranya Abeer Khan Prapya, Maria Sultan

Organizations: BAF Shaheen College Kurmitola · St. Joseph Higher Secondary School · Dhaka Residential Model College · South Point School & College

Abstract

This study develops an integrated, reproducible design assessment for a four-wheel educational rover intended for the high-school remote-control division of the 2027 NASA Human Exploration Rover Challenge. The architecture combines commercial differential drive control, independently sprung wheel carriers, separate drive and task batteries, and a Raspberry Pi-based subsystem that executes received imaging and ranging commands. The analysis reconciles component lists with dimensioned computer-aided design views, derives mobility and electrical constraints, and connects sensor geometry to command execution. At the estimated mass of 10.919 kg, a 215 mm wheel rotating at 200 RPM produces a theoretical rolling speed of 8.105 km/h; consequently, the nominal motor label does not establish compliance with an 8 km/h design ceiling. A steady 20-degree climb requires at least 1.255 newton-meters per wheel under an assumed rolling-resistance coefficient of 0.10 and equal torque sharing. At 50 ft, an ideal 12-inch feature spans approximately 71 pixels across a full-resolution Camera Module 3 Standard image, while a 1-degree pointing error displaces the ranging ray by approximately 266 mm. An executed synthetic benchmark compares color-geometry rules and two linear support-vector classifiers on 2,400 generated board crops with scene-disjoint evaluation. A separate reference command executor is checked using mocked hardware. These software results and analytical sensitivities support a testable development plan; the study does not report physical rover trials. The principal contribution is a traceable connection between component choices, failure mechanisms, quantitative design limits, and reproducible verification tasks.

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